부산대학교 도서관

Classification of coronary artery stenosis is essential in assisting physicians in diagnosing cardiovascular diseases. However, due to the complexity of medical diagnosis and the confidentiality of medical images, it is difficult to obtain many coronary artery stenosis image samples for scientific research in general. In addition, the degree, location, and morphology of coronary artery stenosis in different patients, as well as the noise in computed tomography angiography (CTA) images, make it challenging to extract typing features effectively. To address the above problems, first, a joint segmentation method is proposed based on maximum between-class variance and region growing to extract key regions from CTA images to facilitate further feature extraction. Then, a coronary artery stenosis classification model based on convolutional block attention module (CBAM) and transfer learning is constructed, which can effectively improve the model training effect under the insufficient samples. Finally, a dataset of CTA images from actual patients is applied for experimental verification. The experimental results show that the accuracy (Acc) of the proposed method is up to 98.99%, which is greatly improved compared with several machine learning algorithms and neural network methods. It can be concluded that the accuracy of coronary stenosis classification can be considerably improved, and a reasonable scientific basis is provided for clinical medical diagnosis.